Causal Multi-Label Feature Selection in Federated Setting

TL;DR

This paper introduces FedCMFS, a federated causal multi-label feature selection algorithm that preserves data privacy while effectively identifying relevant features in high-dimensional multi-label data across distributed sources.

Contribution

The paper proposes a novel federated causal multi-label feature selection method with three subroutines, addressing data privacy and feature relevance in distributed multi-label datasets.

Findings

FedCMFS outperforms existing methods on 8 datasets.

Effectively identifies relevant features without centralizing data.

Preserves data privacy during feature selection.

Abstract

Multi-label feature selection serves as an effective mean for dealing with high-dimensional multi-label data. To achieve satisfactory performance, existing methods for multi-label feature selection often require the centralization of substantial data from multiple sources. However, in Federated setting, centralizing data from all sources and merging them into a single dataset is not feasible. To tackle this issue, in this paper, we study a challenging problem of causal multi-label feature selection in federated setting and propose a Federated Causal Multi-label Feature Selection (FedCMFS) algorithm with three novel subroutines. Specifically, FedCMFS first uses the FedCFL subroutine that considers the correlations among label-label, label-feature, and feature-feature to learn the relevant features (candidate parents and children) of each class label while preserving data privacy without centralizing data. Second, FedCMFS employs the FedCFR subroutine to selectively recover the missed true relevant features. Finally, FedCMFS utilizes the FedCFC subroutine to remove false relevant features. The extensive experiments on 8 datasets have shown that FedCMFS is effect for causal multi-label feature selection in federated setting.